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Hiwonder
Hiwonder JetAuto ROS Robot Car Powered by Jetson Nano with Lidar Depth Camera Touch Screen, Support SLAM Mapping and Navigation
Product SKU: HW2019223
- Powered by NVIDIA Jetson Nano and based on ROS
- Support depth camera and Lidar for mapping and navigation
- Optional 7-inch touch screen for parameter monitoring and debugging
- Optional 6-microphone array for voice interaction
- Open-source, and ample PDF materials and tutorials are provided
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Product Description
JetAuto is an entry-level ROS robot car tailored for ROS education. It is loaded with NVIDIA Jetson Nano, high-performance encoder motor, rotatable pan-tilt, Lidar, 3D depth camera and 7-inch screen, which opens up more functionalities. Robot motion control, mapping and navigation, path planning, tracking and obstacle avoidance, autonomous driving, human feature recognition, somatosensory interaction and voice interaction all can be achieved!
Diverse combination of the hardware makes JetAuto an ideal platform where you can learn and verify robotic SLAM function, as well as get the solution to ROS development. Massive ROS learning materials and tutorials are provided to help you get started quickly!
Diverse combination of the hardware makes JetAuto an ideal platform where you can learn and verify robotic SLAM function, as well as get the solution to ROS development. Massive ROS learning materials and tutorials are provided to help you get started quickly!
JetAuto is an entry-level ROS robot car tailored for ROS education. It is loaded with NVIDIA Jetson Nano, high-performance encoder motor, rotatable pan-tilt, Lidar, 3D depth camera and 7-inch screen, which opens up more functionalities. Robot motion control, mapping and navigation, path planning, tracking and obstacle avoidance, autonomous driving, human feature recognition, somatosensory interaction and voice interaction all can be achieved!
Diverse combination of the hardware makes JetAuto an ideal platform where you can learn and verify robotic SLAM function, as well as get the solution to ROS development. Massive ROS learning materials and tutorials are provided to help you get started quickly!
Diverse combination of the hardware makes JetAuto an ideal platform where you can learn and verify robotic SLAM function, as well as get the solution to ROS development. Massive ROS learning materials and tutorials are provided to help you get started quickly!
Jetson Nano Control System
NVIDIA Jetson Nano is able to run mainstream deep learning frameworks, such as TensorFlow, PyTorch, Caffe/ Caffe2, Keras, MXNet. Provide powerful computing power for massive AI projects. Powered by Jetson Nano, JetAuto can implement image recognition, object detection and positioning, pose estimation, semantics segmentation, intelligent analysis and other almighty functions.
NVIDIA Jetson Nano is able to run mainstream deep learning frameworks, such as TensorFlow, PyTorch, Caffe/ Caffe2, Keras, MXNet. Provide powerful computing power for massive AI projects. Powered by Jetson Nano, JetAuto can implement image recognition, object detection and positioning, pose estimation, semantics segmentation, intelligent analysis and other almighty functions.
Lidar Functions Lidar Functions
2D Lidar Mapping and Navigation
JetAuto is loaded with high-performance Lidar that supports mapping with diverse algorithms including Gmapping, Hector, Karto and Cartographer. In addition, path planning, fixed-point navigation as well as obstacle avoidance amid navigation can be implemented.
Single-point Navigation, Multi-point Navigation
JetAuto employs Lidar to detect the surroundings in real time to achieve single-point navigation as well as multi-point navigation.
TEB Path Planning, Obstacle Avoidance
It supports TEB path planning, and is able to monitor the obstacle in real time during navigation. Therefore, it can replan the route to avoid the obstacle and continue moving.
RRT Autonomous Exploration Mapping
Adopting RRT algorithm, JetAuto can complete exploration mapping, save the map and drive back to the starting point autonomously, so there is no need for manual control.
2D Lidar Mapping and Navigation
JetAuto is loaded with high-performance Lidar that supports mapping with diverse algorithms including Gmapping, Hector, Karto and Cartographer. In addition path planning, fixed-point navigation as well as obstacle avoidance amid navigation can be implemented.
Single-point Navigation, Multi-point Navigation
JetAuto employs Lidar to detect the surroundings in real time to achieve single-point navigation as well as multi-point navigation.
TEB Path Planning, Obstacle Avoidance
It supports TEB path planning, and is able to monitor the obstacle in real time during navigation. Therefore, it can replan the route to avoid the obstacle and continue moving.
RRT Autonomous Exploration Mapping
Adopting RRT algorithm, JetAuto can complete exploration mapping, save the map and drive back to the starting point autonomously, so there is no need for manual control.
Lidar Tracking
By scanning the front moving object, Lidar makes robot capable of target tracking.
Lidar Guarding
Guard the surroundings and ring the alarm when detecting intruder.
Depth Camera Depth Camera
RTAB-VSLAM 3D Mapping and Navigation
Depth camera supports 3D mapping in two ways, pure RTAB vision and fusion of vision and Lidar, which allows JetAuto to navigate and avoid obstacle in 3D map, as well as re-locate globally.
ORBSLAM2 + ORBSLAM3
ORB-SLAM is an open-source SLAM framework for monocular, binocular and RGB-D cameras, which is able to compute the camera trajectory in real time and reconstruct 3D surroundings. And under RGB-D mode, the real dimension of the object can be acquired.
Depth Map Data, Point Cloud
Through the corresponding API, JetAuto can get depth map, color image and point cloud of the camera.
RTAB-VSLAM 3D Mapping and Navigation
Depth camera supports 3D mapping in two ways, pure RTAB vision and fusion of vision and Lidar, which allows JetAuto to navigate and avoid obstacle in 3D map, as well as re-locate globally.
ORBSLAM2 + ORBSLAM3
ORB-SLAM is an open-source SLAM framework for monocular, binocular and RGB-D cameras, which is able to compute the camera trajectory in real time and reconstruct 3D surroundings. And under RGB-D mode, the real dimension of the object can be acquired.
Depth Map Data, Point Cloud
Through the corresponding API, JetAuto can get depth map, color image and point cloud of the camera.
Deep Learning, Autonomous Driving
Deep Learning, Autonomous Driving
With JetAuto, you can design an autonomous driving scenario to put ROS into practice, which enables you to better understand core functions of autonomous driving.
With JetAuto, you can design an autonomous driving scenario to put ROS into practice, which enables you to better understand core functions of autonomous driving.
Road Sign Detection
Road Sign Detection
Through training the deep learning model library, JetAuto can realize autonomous driving with AI vision.
Lane Keeping
Lane Keeping
JetAuto is capable of recognizing the lanes on both sides to maintain safe distance between it and the lanes.
Automatic Parking
Automatic Parking
Combined with deep learning algorithm, JetAuto can recognize the parking sign, then steers itself into the slot automatically.
Turning Decision Making
Turning Decision Making
According to the lanes, road signs and traffic lights, JetAuto will estimate the traffic and decide whether to turn.
MediaPipe Development, Upgraded AI Interaction
MediaPipe Development, Upgraded AI Interaction
Based on MediaPipe framework, JetAuto can carry out human body recognition, fingertip detection, face detection, 3D detection and more.
Based on MediaPipe framework, JetAuto can carry out human body recognition, fingertip detection, face detection, 3D detection and more.
Fingertip Trajectory Recognition
Fingertip Trajectory Recognition
Human Body Recognition
Human Body Recognition
3D Detection
3D Detection
3D Face Detection
3D Face Detection
AI Deep Learning Framework
Utilize YOLO network algorithm and deep learning model library to recognize the objects.
KCF Target Tracking
Relying on KCF filtering algorithm, the robot can track the selected target.
Color/Tag Recognition and Tracking
JetAuto is able to recognize and track the designated color, and can recognize multiple April Tags and their coordinates.
Augmented Reality (AR)
After you select the patterns on the APP, the patterns can be overlaid on the April Tag.
Far-field Microphone Array Far-field Microphone
Array
This 6-microphone array is adroit at far-field sound source localization, voice recognition and voice interaction. In comparison to ordinary microphone module, it can implement more advanced functions.
This 6-microphone array is adroit at far-field sound source localization, voice recognition and voice interaction. In comparison to ordinary microphone module, it can implement more advanced functions.
Sound Source Localization
Sound Source Localization
Voice Interaction
Voice Navigation
InterconnectedMotorcade Interconnected
Motorcade
Multi-vehicle Navigation
Depending on multi-machine communication, JetAuto can achieve multi-vehicle navigation, path planning and smart obstacle avoidance.
Depending on multi-machine communication, JetAuto can achieve multi-vehicle navigation, path planning and smart obstacle avoidance.
Intelligent Formation
A batch of JetAuto cars can maintain the formation, including horizontal line, vertical line and triangle, during moving.
Group Control
A group of JetAuto can be controlled by only one wireless handle to perform actions uniformly and simultaneously.
ROS Robot Operating System
Global Popular Robotic Communication Framework
Global Popular Robotic Communication Framework
ROS is an open-source meta operating system for robots. It provides some basic services, such as hardware abstraction, low-level device control, implementation of commonly used functionality, message-passing between processes, and package management. And it also offers the tools and library functions needed to obtain, compile, write, and run code across computers. It aims at providing code reuse support for robotics research and development.
ROS is an open-source meta operating system for robots. It provides some basic services, such as hardwareabstraction, low-level device control, implementation of commonly used functionality, message-passingbetween processes, and package management.
And it also offers the tools and library functions needed toobtain, compile, write, and run code across computers. It aims at providing code reuse support for robotics research and development.
And it also offers the tools and library functions needed toobtain, compile, write, and run code across computers. It aims at providing code reuse support for robotics research and development.
Gazebo Simulation
Gazebo Simulation
JetAuto employs ROS framework and supports Gazebo simulation. Gazebo brings a fresh approach for you to control JetAuto and verify the algorithm in simulated environment, which reduces experimental requirements and improves efficiency.
JetHexa employs ROS framework and supports Gazebo simulation. Gazebo brings a fresh approach for you to control JetHexa and verify the algorithm in simulated environment, which reduces experimental requirements and improves efficiency.
JetAuto Simulation Control
The kinematics algorithm can be verified in simulation to speed up algorithm iteration and reduce the experiment cost.
Visual Data
RViz can visualize the mapping and navigation result, which facilitates debugging and improving algorithm.
Various Control Methods
Various Control Methods
WonderAi APP
Map Nav APP (Android Only)
Wireless Handle
Product Structure
SLAMTEC A1 Lidar
SLAMTEC A1 Lidar
ORBBEC Astra Pro Depth Camera
ORBBEC Astra Pro Depth Camera
iFLYTEK Far-field Microphone Array Module
iFLYTEK Far-field Microphone Array Module
HD 7-inch LCD Touch Screen
HD 7-inch LCD Touch Screen
Intelligent Serial Bus Servo
Intelligent Serial Bus Servo
Core Accessories
Core Accessories
Multi-functional Expansion Board
The expansion board has a built-in IMU sensor which can detect robot posture in real time. There are 2-channel PWM, two keys, a LED, a buzzer, 9-channel serial bus servo interface, two GPIO expansion ports and two IIC interfaces on it.
Hall Encoder Geared Motor
520 motor comes with high-accuracy encoder, and features strong force and high performance. The built-in AB phase incremental Hall encoder stands out for its high accuracy and anti-interference ability.
11.1 V 6000mAh Lipo Battery
This large-capacity Lipo battery can better power the robot and prolongs robot’s working life.
Encoder Motor Driver
JetAuto is equipped with 4-channel encoder motor driver making it easier to control motor.
Product Parameter
JetAuto Starter Kit
JetAuto Standard Kit
JetAuto Advanced Kit
| Item | Specification |
| Size | 302*260*256mm |
| Weight | 3.5kg |
| Material | Full-metal hard aluminum alloy bracket(anodized) |
| Battery | 11.1V 6000mAh Lipo battery |
| Continuous working life | 60min |
| Hardware | ROS controller and ROS expansion board |
| Operating system | Ubuntu 18.04 LTS + ROS Melodic |
| Software | iOS/Android APP |
| Communication | USB/WiFi/Ethernet |
| Programming language | Python/C/C++/JavaScript |
| Storage | 32GB TF card |
| Servo | HTS-20H serial bus servo |
| Control method | Phone/ Handle control |
| Package size (advanced kit) | 335*320*225mm |
| Package weight (advanced kit) | about 4.5kg |
| Hall Encoder Geared Motor | Rated voltage: 12V Rated power consumption: 2.4W Motor type: permanent-magnet brush Stall current: 3A Stall torque: 15kgf.cm Rated current: 0.2A Rated torque: 10kgf.cm Reduction ratio: 1 : 90 Rotation speed (before reduction): 10000rpm Rotation speed (after reduction): 110± 10rpm Output shaft: D-type eccentric shaft of 6 mm diameter Encoder type: Hall encoder Interface: PH2.0 - 6P Power supply: 3.3 - 5V |
| Lipo Battery | Voltage: 11.1V Capacity: 6000mAh Plug: DC 5.5 * 2.5 female/ SM-2P male |
QUESTIONS & ANSWERS
Ask a Question-
Hi I am a researcher at York University in Toronto, Canada. I am interested in your product "Hiwonder JetAuto" for a use in multi-robot configurations. In this context, I want to have more details on the communication architectures that are possible through WIFI. Can we stablish decentralized talking, where each robot can individually and independently talk to a neigboring robot without having to route the message through a WIFI router ???? I noticed each vehicle is equipped with antennas, do you think we can make the antennas exchange signals directly without linking to a central router ? If not then routing all the vehicles through a central router within a standard WIFI network to establish communications is required, in that case, what do you recommend to manage communication protocols and priorities for multiple robots talking simultaneously ? Do you have a custom solution ? Thank you very much.
Under AP direct connection mode, it is possible to interconnect multiple JetAutos. However, it is important to note that the quality of the connection may not be as optimal as when using a router. In order to ensure efficient multi-robot control and formation, we typically utilize ROS distributed communication. This application necessitates a high-quality communication bandwidth. Therefore, we recommend using a router for optimal performance and reliable operation.
